13,445 research outputs found
Understanding and Diagnosing Visual Tracking Systems
Several benchmark datasets for visual tracking research have been proposed in
recent years. Despite their usefulness, whether they are sufficient for
understanding and diagnosing the strengths and weaknesses of different trackers
remains questionable. To address this issue, we propose a framework by breaking
a tracker down into five constituent parts, namely, motion model, feature
extractor, observation model, model updater, and ensemble post-processor. We
then conduct ablative experiments on each component to study how it affects the
overall result. Surprisingly, our findings are discrepant with some common
beliefs in the visual tracking research community. We find that the feature
extractor plays the most important role in a tracker. On the other hand,
although the observation model is the focus of many studies, we find that it
often brings no significant improvement. Moreover, the motion model and model
updater contain many details that could affect the result. Also, the ensemble
post-processor can improve the result substantially when the constituent
trackers have high diversity. Based on our findings, we put together some very
elementary building blocks to give a basic tracker which is competitive in
performance to the state-of-the-art trackers. We believe our framework can
provide a solid baseline when conducting controlled experiments for visual
tracking research
The Coordinate Particle Filter - A novel Particle Filter for High Dimensional Systems
Parametric filters, such as the Extended Kalman Filter and the Unscented
Kalman Filter, typically scale well with the dimensionality of the problem, but
they are known to fail if the posterior state distribution cannot be closely
approximated by a density of the assumed parametric form. For nonparametric
filters, such as the Particle Filter, the converse holds. Such methods are able
to approximate any posterior, but the computational requirements scale
exponentially with the number of dimensions of the state space. In this paper,
we present the Coordinate Particle Filter which alleviates this problem. We
propose to compute the particle weights recursively, dimension by dimension.
This allows us to explore one dimension at a time, and resample after each
dimension if necessary. Experimental results on simulated as well as real data
confirm that the proposed method has a substantial performance advantage over
the Particle Filter in high-dimensional systems where not all dimensions are
highly correlated. We demonstrate the benefits of the proposed method for the
problem of multi-object and robotic manipulator tracking
Riemann-Langevin Particle Filtering in Track-Before-Detect
Track-before-detect (TBD) is a powerful approach that consists in providing
the tracker with sensor measurements directly without pre-detection. Due to the
measurement model non-linearities, online state estimation in TBD is most
commonly solved via particle filtering. Existing particle filters for TBD do
not incorporate measurement information in their proposal distribution. The
Langevin Monte Carlo (LMC) is a sampling method whose proposal is able to
exploit all available knowledge of the posterior (that is, both prior and
measurement information). This letter synthesizes recent advances in LMC-based
filtering to describe the Riemann-Langevin particle filter and introduces its
novel application to TBD. The benefits of our approach are illustrated in a
challenging low-noise scenario.Comment: Minor grammatical update
A Multi-Scan Labeled Random Finite Set Model for Multi-object State Estimation
State space models in which the system state is a finite set--called the
multi-object state--have generated considerable interest in recent years.
Smoothing for state space models provides better estimation performance than
filtering by using the full posterior rather than the filtering density. In
multi-object state estimation, the Bayes multi-object filtering recursion
admits an analytic solution known as the Generalized Labeled Multi-Bernoulli
(GLMB) filter. In this work, we extend the analytic GLMB recursion to propagate
the multi-object posterior. We also propose an implementation of this so-called
multi-scan GLMB posterior recursion using a similar approach to the GLMB filter
implementation
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